The low-rank tensor factorization (LRTF) technique has received increasing popularity in data science, especially in computer vision applications. Many robust LRTF models have been presented recently. However, none of them take the skewness of data into account. This letter proposes a novel LRTF model for skew data analysis by modeling noise as a Mixture of Asymmetric Laplacians (MoAL). The numerical experiments show that the new model MoAL-LRTF outperforms several state-of-the-art counterparts. The codes for all the experiments are available at https://xsxjtu.github.io/Projects/MoAL/main.html.

中文翻译：

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具有歪斜噪声的稳健 CP 张量分解

低秩张量分解 (LRTF) 技术在数据科学中越来越受欢迎，尤其是在计算机视觉应用中。最近已经提出了许多强大的 LRTF 模型。然而，他们都没有考虑数据的偏度。这封信通过将噪声建模为非对称拉普拉斯算子 (MoAL) 的混合物，提出了一种新颖的 LRTF 模型，用于偏斜数据分析。数值实验表明，新模型 MoAL-LRTF 优于几个最先进的模型。所有实验的代码都可以在 https://xsxjtu.github.io/Projects/MoAL/main.html 获得。